Bangaly Kaba

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Recent work has used graphs to modelize expression data from microarray experiments, in view of partitioning the genes into clusters. In this paper, we introduce the use of a decomposition by clique separators. Our aim is to improve the classical clustering methods in two ways: first we want to allow an overlap between clusters, as this seems biologically(More)
Algorithm CPLCL (Classification by Preferential Clustered Link) for the classification graphs of syntactic variations, introduced by Ibekwe-SanJuan in [10, 6], has the mathematical properties of single link clustering, since it is partly founded on the concept of component related to a subgraph, while avoiding the chain effect as much as possible. In spite(More)
We propose a graph-based decomposition methodology of a network of document features represented by a terminology graph. The graph is automatically extracted from raw data based on Natural Language Processing techniques implemented in the TermWatch system. These graphs are Small Worlds. Based on clique minimal separators and the associated graph of atoms: a(More)